Qualified Health and the University of Texas System use Claude to identify patients who need life-saving care

Texas faces critical physician shortages and healthcare systems struggle to identify at-risk patients buried in fragmented clinical data; an estimated 4-6 million patients annually qualify for evidence-based interventions but go unidentified. Qualified Health built an AI platform using Claude Sonnet 4.5 to screen patient populations, integrating notes, labs, imaging and procedural records against guideline-based criteria to surface eligible patients into clinician workflows with documentation traceable to source data. The platform now screens a 1 million+ patient population at The University of Texas Medical Branch; in its first month it found that as many as a third of heart failure patients had opportunities for improved medication optimization, and it surfaces dozens of eligible patients per review cycle. Expansion to six specialties (primary care, vascular, GI, rheumatology, neurology) is planned by end of 2026.

Anthropic builds the Claude model family, including a dedicated Claude for Life Sciences push used across drug discovery, regulatory writing and clinical documentation.

This sits in the Build stage of the AI use-case lifecycle: Named examples from the foundation-model labs and platforms pharma teams use to actually construct, train and generate the model or application.